Abdominal lymphadenopathy detection using random forest

We propose a new method for detecting abdominal lymphadenopathy by utilizing a random forest statistical classifier to create voxel-level lymph node predictions, i.e. initial detection of enlarged lymph nodes. The framework permits the combination of multiple statistical lymph node descriptors and appropriate feature selection in order to improve lesion detection beyond traditional enhancement filters. We show that Hessian blobness measurements alone are inadequate for detecting lymph nodes in the abdominal cavity. Of the features tested here, intensity proved to be the most important predictor for lymph node classification. For initial detection, candidate lesions were extracted from the 3D prediction map generated by random forest. Statistical features describing intensity distribution, shape, and texture were calculated from each enlarged lymph node candidate. In the last step, a support vector machine (SVM) was trained and tested based on the calculated features from candidates and labels determined by two experienced radiologists. The computer-aided detection (CAD) system was tested on a dataset containing 30 patients with 119 enlarged lymph nodes. Our method achieved an AUC of 0.762±0.022 and a sensitivity of 79.8% with 15 false positives suggesting it can aid radiologists in finding enlarged lymph nodes.